Performance of Artificial Intelligence Models Designed for Automated Estimation of Age Using Dento-Maxillofacial Radiographs-A Systematic Review.

Automatic age estimation has garnered significant interest among researchers because of its potential practical uses. The current systematic review was undertaken to critically appraise developments and performance of AI models designed for automated estimation using dento-maxillofacial radiographic images. In order to ensure consistency in their approach, the researchers followed the diagnostic test accuracy guidelines outlined in PRISMA-DTA for this systematic review. They conducted an electronic search across various databases such as PubMed, Scopus, Embase, Cochrane, Web of Science, Google Scholar, and the Saudi Digital Library to identify relevant articles published between the years 2000 and 2024. A total of 26 articles that satisfied the inclusion criteria were subjected to a risk of bias assessment using QUADAS-2, which revealed a flawless risk of bias in both arms for the patient-selection domain. Additionally, the certainty of evidence was evaluated using the GRADE approach. AI technology has primarily been utilized for automated age estimation through tooth development stages, tooth and bone parameters, bone age measurements, and pulp-tooth ratio. The AI models employed in the studies achieved a remarkably high precision of 99.05% and accuracy of 99.98% in the age estimation for models using tooth development stages and bone age measurements, respectively. The application of AI as an additional diagnostic tool within the realm of age estimation demonstrates significant promise.

Segmentation of Ocular Thermogram Using Level-set Algorithm for Analysis of Contralateral Portions in Healthy Eyes

OBJECTIVE: This work aimed to evaluate the level set segmentation algorithm on ocular surface thermograms. In addition, the vascularity functioning between the contralateral portions of two eyes (right and left) was identified using statistical analysis methods. METHODS: A total of 25 healthy participants with an average age of 35 years (20 men and 5 women) were selected in April 2022. Thermogram images were captured using a FLIR T series thermal camera. Conventional image processing techniques, such as filtering and edge detection, were used to preprocess thermograms. Next, the level set approach was used with the edge-detected pattern as an input to an automated segmented region of interest (ROI). RESULTS: Five metrics, namely Dice Coefficient, Tanimoto Index, Jaccard Index, Volume Similarity, and Structural Similarity, were used to assess the performance of the segmentation technique compared to ground truth, which showed 97.5%, 92.5%, 94.5%, 96.5%, and 96.5% correlation, respectively, between the segmented and the ground truth images with average values for both the eyes. Statistical analysis demonstrated that the contralateral portions of the ocular thermograms were significantly different in terms of vascular distribution between the left and right eyes (p < 0.005) CONCLUSION: The level set method efficiently segmented the ROI in ocular thermograms with maximum correlation. According to the segmentation's results, the model showed the dissimilarity between the contralateral parts of the left and right eyes in healthy cases.

Breast cancer classification based on convolutional neural network and image fusion approaches using ultrasound images.

Deep learning and image processing are used to classify and segment breast tumor images, specifically in ultrasound (US) modalities, to support clinical decisions and improve healthcare quality. However, directly using US images can be challenging due to noise and diverse imaging modalities. In this study, we developed a three-step image processing scheme involving speckle noise filtering using a block-matching three-dimensional filtering technique, region of interest highlighting, and RGB fusion. This method enhances the generalization of deep-learning models and achieves better performance. We used a deep learning model (VGG19) to perform transfer learning on three datasets: BUSI (780 images), Dataset B (162 images), and KAIMRC (5693 images). When tested on the BUSI and KAIMRC datasets using a fivefold cross-validation mechanism, the model with the proposed preprocessing step performed better than without preprocessing for each dataset. The proposed image processing approach improves the performance of the breast cancer deep learning classification model. Multiple diverse datasets (private and public) were used to generalize the model for clinical application.

Visual Functions, Seatbelt Usage, Speed, and Alcohol Consumption Standards for Driving and Their Impact on Road Traffic Accidents.

Purpose:  The incidence of road traffic accidents (RTAs) is dramatically increasing worldwide. Consequently, driving and licensing authorities have instituted strict rules and regulations, such as vision standards, restrictions on drunk driving, seat belt usage, and speeding, for driving safety. This study aimed to summarize the global visual standards for driving license issuing and renewal and investigate the effect of driving safety laws on RTA-related death rates in different countries. Methods:  The study gathered data on visual standards for driving licenses from reliable sources and extracted enforcement scores (drunk driving, seat belt usage, and speeding) and RTA-related death rates from the World Health Organization status report on road safety. The Wilcoxon test explored the association between visual standards and RTA-related death rates, while the Kruskal-Wallis test analyzed the relationship between visual functions and death rates, as well as driving safety enforcement scores and RTA-related death rates. Results:  The analysis was conducted on 71 countries and 50 states within the United States out of the 193 countries listed by the United Nations. It was found that 116 countries and states required a minimum VA range of 6/6-6/18, while 91 countries and states mandated a similar range for one-eyed drivers. VF testing for driving licenses was necessary in 77 countries and states. No significant association was observed between VA or VF testing and RTA-related death rates. However, countries that conducted more visual function tests demonstrated lower rates of RTA-related fatalities. Furthermore, RTA-related death rates were significantly associated with speeding, drunk driving, and seat belt laws. Conclusion: Implementing clear policies regarding vision requirements, maintaining strict rules, and promoting law enforcement on speeding, drunk driving, and seat belt usage are crucial for improving road safety. These measures should be prioritized by driving and licensing authorities worldwide to mitigate the escalating incidence of RTAs.

The Patients' Perspective for the Impact of Late Detection of Ocular Diseases on Quality of Life: A Cross-Sectional Study.

Background: Late detection of ocular diseases negatively affects patients' quality of life (QoL), encompassing health status, psychological, financial, and social aspects. However, the early detection of eye conditions leads to rapid intervention and avoiding complications, thus preserving the QoL. This study assessed the impact of ocular diseases late detection on patients' QoL at multi-eye clinics based on questionnaire responses. Methods: We developed an original Arabic-English questionnaire to assess the QoL of patients with ocular diseases referred from primary and secondary healthcare centers to tertiary hospitals. It covered preliminary data, patient perspectives on having lately detected ocular disease and treatment costs, and the impact of late detection on finances, social life, psychology, health status, and awareness of current initiatives. Logistic regression analysis was used to explore the associations between patient perspectives on having ocular diseases detected at a late stage and its impact on different domains. Multivariate logistic regression was applied with impact types of health status, psychological, financial, and social (dependent variables) and age, income levels, and hospital type (independent variables). Results: Three hundred and eighty-eight responded, with 50% experiencing psychological effects, 27% health issues, 23% social impacts, and 23% financial burdens. Two hundred seventeen patients (56%) reported having ocular condition detected in late stage. Logistic regression analysis showed positive association with health status, social well-being, and financial effects (p < 0.05). Multivariate analysis revealed pronounced effects in patients ≤ 50 years, with income \< 5000 SAR, and those visiting private clinics (p < 0.05). The social impact was greater in patients visiting private hospitals. Ninety percent of all patients emphasized the importance of increasing awareness for better QoL. Conclusion: Significant associations were found between the late detection of eye diseases and their impact on QoL. Therefore, early detection and increasing patients' awareness of ocular diseases and treatment are essential.

Impact of Genetic Variations on Thromboembolic Risk in Saudis with Sickle Cell Disease.

BACKGROUND: Sickle cell disease (SCD) is a Mendelian disease characterized by multigenic phenotypes. Previous reports indicated a higher rate of thromboembolic events (TEEs) in SCD patients. A number of candidate polymorphisms in certain genes (e.g., FVL, PRT, and MTHFR) were previously reported as risk factors for TEEs in different clinical conditions. This study aimed to genotype these genes and other loci predicted to underlie TEEs in SCD patients. METHODOLOGY: A multi-center genome-wide association study (GWAS) involving Saudi SCD adult patients with a history of TEEs (n = 65) and control patients without TEE history (n = 285) was performed. Genotyping used the 10× Affymetrix Axiom array, which includes 683,030 markers. Fisher's exact test was used to generate p-values of TEE associations with each single-nucleotide polymorphism (SNP). The haplotype analysis software tool version 1.05, designed by the University of Göttingen, Germany, was used to identify the common inherited haplotypes. RESULTS: No association was identified between the targeted single-nucleotide polymorphism rs1801133 in MTHFR and TEEs in SCD (p = 0.79). The allele frequency of rs6025 in FVL and rs1799963 in PRT in our cohort was extremely low (<0.01); thus, both variants were excluded from the analysis as no meaningful comparison was possible. In contrast, the GWAS analysis showed novel genome-wide associations (p < 5 × 10-8) with seven signals; five of them were located on Chr 11 (rs35390334, rs331532, rs317777, rs147062602, and rs372091), one SNP on Chr 20 (rs139341092), and another on Chr 9 (rs76076035). The other 34 SNPs located on known genes were also detected at a signal threshold of p < 5 × 10-6. Seven of the identified variants are located in olfactory receptor family 51 genes (OR51B5, OR51V1, OR51A1P, and OR51E2), and five variants were related to family 52 genes (OR52A5, OR52K1, OR52K2, and OR52T1P). The previously reported association between rs5006884-A in OR51B5 and fetal hemoglobin (HbF) levels was confirmed in our study, which showed significantly lower levels of HbF (p = 0.002) and less allele frequency (p = 0.003) in the TEE cases than in the controls. The assessment of the haplotype inheritance pattern involved the top ten significant markers with no LD (rs353988334, rs317777, rs14788626882, rs49188823, rs139349992, rs76076035, rs73395847, rs1368823, rs8888834548, and rs1455957). A haplotype analysis revealed significant associations between two haplotypes (a risk, TT-AA-del-AA-ins-CT-TT-CC-CC-AA, and a reverse protective, CC-GG-ins-GG-del-TT-CC-TT-GG-GG) and TEEs in SCD (p = 0.024, OR = 6.16, CI = 1.34-28.24, and p = 0.019, OR = 0.33, CI = 0.13-0.85, respectively). CONCLUSIONS: Seven markers showed novel genome-wide associations; two of them were exonic variants (rs317777 in OLFM5P and rs147062602 in OR51B5), and less significant associations (p < 5 × 10-6) were identified for 34 other variants in known genes with TEEs in SCD. Moreover, two 10-SNP common haplotypes were determined with contradictory effects. Further replication of these findings is needed.

Peripapillary atrophy classification using CNN deep learning for glaucoma screening.

Glaucoma is the second leading cause of blindness worldwide, and peripapillary atrophy (PPA) is a morphological symptom associated with it. Therefore, it is necessary to clinically detect PPA for glaucoma diagnosis. This study was aimed at developing a detection method for PPA using fundus images with deep learning algorithms to be used by ophthalmologists or optometrists for screening purposes. The model was developed based on localization for the region of interest (ROI) using a mask region-based convolutional neural networks R-CNN and a classification network for the presence of PPA using CNN deep learning algorithms. A total of 2,472 images, obtained from five public sources and one Saudi-based resource (King Abdullah International Medical Research Center in Riyadh, Saudi Arabia), were used to train and test the model. First the images from public sources were analyzed, followed by those from local sources, and finally, images from both sources were analyzed together. In testing the classification model, the area under the curve's (AUC) scores of 0.83, 0.89, and 0.87 were obtained for the local, public, and combined sets, respectively. The developed model will assist in diagnosing glaucoma in screening programs; however, more research is needed on segmenting the PPA boundaries for more detailed PPA detection, which can be combined with optic disc and cup boundaries to calculate the cup-to-disc ratio.

Therapeutics discovery: From bench to first in-human trials.

The 'Therapeutics discovery: From bench to first in-human trials' conference, held at the King Abdullah International Medical Research Center (KAIMRC), Ministry of National Guard Health Affairs (MNGHA), Kingdom of Saudi Arabia (KSA) from October 10-12, 2017, provided a unique opportunity for experts worldwide to discuss advances in drug discovery and development, focusing on phase I clinical trials. It was the first event of its kind to be hosted at the new research center, which was constructed to boost drug discovery and development in the KSA in collaboration with institutions, such as the Academic Drug Discovery Consortium in the United States of America (USA), Structural Genomics Consortium of the University of Oxford in the United Kingdom (UK), and Institute of Materia Medica of the Chinese Academy of Medical Sciences in China. The program was divided into two parts. A pre-symposium day took place on October 10, during which courses were conducted on clinical trials, preclinical drug discovery, molecular biology and nanofiber research. The attendees had the opportunity for one-to-one meetings with international experts to exchange information and foster collaborations. In the second part of the conference, which took place on October 11 and 12, the clinical trials pipeline, design and recruitment of volunteers, and economic impact of clinical trials were discussed. The Saudi Food and Drug Administration presented the regulations governing clinical trials in the KSA. The process of preclinical drug discovery from small molecules, cellular and immunologic therapies, and approaches to identifying new targets were also presented. The recommendation of the conference was that researchers in the KSA must invest more fund, talents and infrastructure to lead the region in phase I clinical trials and preclinical drug discovery. Diseases affecting the local population, such as Middle East Respiratory Syndrome and resistant bacterial infections, represent the optimal starting point.